-

README.md

@@ -1,12 +1,45 @@
----
-title: Inference
-emoji: 🔥
-colorFrom: blue
-colorTo: indigo
-sdk: streamlit
-sdk_version: 1.41.1
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# YOLOv8 Video/Webcam Inference Streamlit App
+
+This is a Streamlit application that uses YOLOv8 models with Ultralytics for real-time inference on videos or webcam feed.
+
+## Requirements
+
+- Python 3.7+
+- Streamlit
+- Ultralytics
+- OpenCV
+
+## Installation
+
+1. Clone the repository:
+
+   ```
+   git clone https://github.com/your-username/yolov8-streamlit-app.git
+   cd yolov8-streamlit-app
+   ```
+
+2. Install the dependencies:
+
+   ```
+   pip install -r requirements.txt
+   ```
+
+## Usage
+
+1. Download the YOLOv8 model file (`yolov8n.pt`) and place it in the project directory.
+
+2. Run the Streamlit app:
+
+   ```
+   streamlit run app.py
+   ```
+
+3. Open the app in your web browser using the provided URL.
+
+4. Upload a video file or select the "Use webcam" option to start the inference.
+
+5. The app will display the inference results on the video frames or webcam feed in real-time.
+
+## License
+
+This project is licensed under the MIT License. 

app.py

@@ -0,0 +1,54 @@
+import streamlit as st
+from ultralytics import YOLO
+import cv2
+from PIL import Image
+from io import BytesIO
+import cairosvg
+
+# Read the SVG logo file
+with open("marca-cor-fundo-escuro.svg", "r") as f:
+    logo_svg = f.read()
+
+# Convert SVG to PNG using cairosvg
+logo_png_bytes = cairosvg.svg2png(bytestring=logo_svg.encode())
+logo_png = Image.open(BytesIO(logo_png_bytes))
+
+# Display the logo
+st.image(logo_png, width=200)
+
+# Load the YOLOv8 model
+model = YOLO('yolov8n.pt')
+
+# Set up the Streamlit app
+#st.title('YOLOv8 Video/Webcam Inference')
+
+# Add file uploader or webcam option
+video_file = st.file_uploader("Upload a video", type=["mp4", "avi"])
+use_webcam = st.checkbox("Use webcam")
+
+# Placeholder for inference results
+inference_placeholder = st.empty()
+
+# Video/webcam inference loop
+if video_file is not None or use_webcam:
+    if video_file is not None:
+        video = cv2.VideoCapture(video_file.name)
+    else:
+        video = cv2.VideoCapture(0)  # Use webcam
+
+    while True:
+        ret, frame = video.read()
+        if not ret:
+            break
+
+        frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+
+        # Run YOLOv8 inference on the frame
+        results = model(frame)
+
+        # Display the inference results
+        inference_placeholder.image(results[0].plot(), use_container_width=True)
+
+    video.release()
+else:
+    st.warning("Please upload a video file or select the webcam option.") 

requirements.txt

@@ -0,0 +1,5 @@
+streamlit
+ultralytics
+opencv-python 
+pillow
+cairosvg